<h4>Tool</h4><table border="0"><tr><td valign="top"><b>Name</b></td><td valign="top">Minimum Redundancy Feature Selection</td></tr><tr><td valign="top"><b>ID</b></td><td valign="top">12</td></tr><tr><td valign="top"><b>Author</b></td><td valign="top">O.Conrad (c) 2014</td></tr></table><hr><h4>Description</h4>Identify the most relevant features for subsequent classification of tabular data.

The minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm has been developed by Hanchuan Peng <hanchuan.peng@gmail.com>.

References:
Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. Hanchuan Peng, Fuhui Long, and Chris Ding, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp.1226-1238, 2005.

Minimum redundancy feature selection from microarray gene expression data,
Chris Ding, and Hanchuan Peng, Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, pp.185-205, 2005.

Hanchuan Peng's mRMR Homepage at <a target="_blank" href="http://penglab.janelia.org/proj/mRMR/">http://penglab.janelia.org/proj/mRMR/</a>
<hr><h4>Parameters</h4><table border="1" width="100%" valign="top" cellpadding="5" rules="all"><tr><th>Name</th><th>Type</th><th>Identifier</th><th>Description</th><th>Constraints</th></tr>
<tr><th colspan="5">Input</th></tr><tr><td>Data </td><td>Table (input)</td><td>DATA</td><td></td><td></td></tr><tr><th colspan="5">Output</th></tr><tr><td>Feature Selection</td><td>Table (output)</td><td>SELECTION</td><td></td><td></td></tr><tr><th colspan="5">Options</th></tr><tr><td>Class Identifier</td><td>Table field</td><td>CLASS</td><td></td><td></td></tr><tr><td>Verbose Output</td><td>Boolean</td><td>VERBOSE</td><td>output of intermediate results to execution message window</td><td>Default: 1</td></tr><tr><td>Number of Features</td><td>Integer</td><td>mRMR_NFEATURES</td><td></td><td>Minimum: 1
Default: 50</td></tr><tr><td>Discretization</td><td>Boolean</td><td>mRMR_DISCRETIZE</td><td>uncheck this means no discretizaton (i.e. data is already integer)</td><td>Default: 1</td></tr><tr><td>Discretization Threshold</td><td>Floating point</td><td>mRMR_THRESHOLD</td><td>a double number of the discretization threshold; set to 0 to make binarization</td><td>Minimum: 0.000000
Default: 1.000000</td></tr><tr><td>Selection Method</td><td>Choice</td><td>mRMR_METHOD</td><td></td><td>Available Choices:
[0] Mutual Information Difference (MID)
[1] Mutual Information Quotient (MIQ)
Default: 0</td></tr></table>